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Investigates developmental epigenomes and transcriptomes that are related to De novo mutations (DNMs) in developmental disorders. EpiDenovo is a database for exploring the associations between embryonic epigenetic regulation and DNMs in developmental disorders, including neuropsychiatric disorders and congenital heart disease. This resource is based on the epigenomes of publicly available chromatin immunoprecipitation sequencing (ChIP-seq) and chromatin accessibility data during the embryonic development of mammals, including humans and mice.


A RNA-sequencing data set. rnaseqmixture provides a valuable resource for benchmarking different protocols and data pre-processing workflows. The classic mixture design allows precision and bias to be quantified via a non-linear model for each gene, and used as a basis for comparing different sample preparation methods. This mixture design also allows for internal comparisons to be made within methods for benchmarking differential expression and differential splicing analysis methods.

TDCA / Time Dependent ChIP-Sequencing Analysis

Facilitates analysis of a wide range of time course (TC) data in an automated manner. TDCA models changes in sequencing coverage of individual loci within TC ChIP-seq, or conceptually related experiments, as a function of time. Several customizable options are available, such as the ability to tune modeling parameters, include genome specific analyses, and specify normalization constants. The software can be applied to obtain insights that are of potential biological importance.

Cistrome DB / Cistrome Data Browser

A collection of ChIP-seq and chromatin accessibility data (DNase-seq and ATAC-seq) including 13366 human and 9953 mouse samples. Cistrome DB contains data which have been carefully curated and processed with a streamlined analysis pipeline and evaluated with comprehensive quality control metrics. A user-friendly web server allows data query, exploration and visualization. Cistrome DB provides a manually curated metadata annotation, presents analysis results, including a peak file, a read density file, motif scan results, putative target genes and summaries of the distribution of peaks. It provides comprehensive QC metrics at the read, peak and annotation levels and provides functions to analyze and visualize these samples. Users can directly send data to the Cistrome analysis pipeline (Cistrome AP) or load data to the UCSC and WashU genome browsers for visualization.

fCCAC / functional Canonical Correlation Analysis to evaluate Covariance

Provides a functional canonical correlation analysis approach. fCCAC method can be used (i) to evaluate reproducibility, and flag datasets showing low canonical correlations; (ii) or to investigate covariation between genetic and epigenetic regulations, to infer their potential functional correlations. It can also be used for developing new hypothesis about how changes in transcription factor (TFs), chromatin remodelling enzymes, histone marks, RNA binding protein and epitranscriptome can cooperatively dictate the specification of cell function and identity.

LPCHP / Linear predictive coding histone profile

Allows the capture and comparison of ChIP-seq histone profiles. LPCHP can be used as an alternative to read intensities, its utility may extend beyond ChIP-seq to other next-generation sequencing (NGS) applications. It can be used in identification of enhancer or regulatory regions in the genome. The tool is robust against changes in p, including cases where it was customized to dataset. LPCHP can identify commonalities between different histone modifications.


Computes a similarity metric between two ChIP-seq datasets to quantify chromatin interactions. In contrast to a basic count of overlaps between two Transcription Factor Binding Sites, IntervalStats allows to compute an exact P-value on their similarity metric. This metric is asymmetric and they demonstrate that it can highlight particular behaviour such as "co-factor" function of a protein. For every query interval, this method produces the closest reference interval, the distance between them and P-value. Their method is insensitive to non-biological variation in datasets (peak width for example). Furthermore, IntervalStats similarity computation can be restricted to a set of genomic regions (such as mappable genome, promoters, open chromatin regions). So it can model peak location biases.

MIM / Motif Independent Metric

Calculates an unbiased quantitative measure for DNA sequence specificity. MIM method has extended previous work by further accounting for sequence specificity due to accumulation of weak sequence features. The information can be used as a guide to systematically investigate the regulatory mechanisms for a wide variety of biological processes. By analyzing both simulated and real experimental data, it was found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs. The MIM algorithm is implemented in Python and can be freely accessed for download.


Helps in clustering motifs identified from mammalian species genome-wide. MotifOrganizer is a two-stage, divide-conquer-combine clustering scheme able to gather motif collections in the hundreds of thousands. This algorithm allows motifs of variable different widths to be clustered together and is capable of handling large scale input motif sets. Its performance was extending to include parameters that address more aspects of eukaryotic transcriptional regulation.